229 lines
4.5 KiB
Python
229 lines
4.5 KiB
Python
import logging
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import os
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import torch
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from torchaudio._internal import download_url_to_file, module_utils as _mod_utils
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def _get_chars():
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return (
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"_",
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"-",
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"!",
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"'",
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"(",
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")",
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",",
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".",
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":",
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";",
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"?",
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" ",
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"a",
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"b",
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"c",
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"d",
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"e",
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"f",
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"g",
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"h",
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"i",
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"j",
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"k",
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"l",
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"m",
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"n",
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"o",
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"p",
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"q",
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"r",
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"s",
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"t",
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"u",
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"v",
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"w",
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"x",
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"y",
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"z",
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)
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def _get_phones():
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return (
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"_",
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"-",
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"!",
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"'",
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"(",
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")",
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",",
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".",
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":",
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";",
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"?",
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" ",
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"AA",
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"AA0",
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"AA1",
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"AA2",
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"AE",
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"AE0",
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"AE1",
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"AE2",
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"AH",
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"AH0",
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"AH1",
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"AH2",
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"AO",
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"AO0",
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"AO1",
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"AO2",
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"AW",
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"AW0",
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"AW1",
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"AW2",
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"AY",
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"AY0",
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"AY1",
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"AY2",
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"B",
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"CH",
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"D",
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"DH",
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"EH",
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"EH0",
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"EH1",
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"EH2",
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"ER",
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"ER0",
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"ER1",
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"ER2",
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"EY",
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"EY0",
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"EY1",
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"EY2",
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"F",
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"G",
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"HH",
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"IH",
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"IH0",
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"IH1",
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"IH2",
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"IY",
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"IY0",
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"IY1",
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"IY2",
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"JH",
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"K",
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"L",
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"M",
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"N",
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"NG",
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"OW",
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"OW0",
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"OW1",
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"OW2",
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"OY",
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"OY0",
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"OY1",
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"OY2",
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"P",
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"R",
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"S",
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"SH",
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"T",
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"TH",
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"UH",
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"UH0",
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"UH1",
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"UH2",
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"UW",
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"UW0",
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"UW1",
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"UW2",
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"V",
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"W",
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"Y",
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"Z",
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"ZH",
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)
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def _to_tensor(indices):
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lengths = torch.tensor([len(i) for i in indices], dtype=torch.int32)
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values = [torch.tensor(i) for i in indices]
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values = torch.nn.utils.rnn.pad_sequence(values, batch_first=True)
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return values, lengths
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def _load_phonemizer(file, dl_kwargs):
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if not _mod_utils.is_module_available("dp"):
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raise RuntimeError("DeepPhonemizer is not installed. Please install it.")
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from dp.phonemizer import Phonemizer
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# By default, dp issues DEBUG level log.
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logger = logging.getLogger("dp")
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orig_level = logger.level
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logger.setLevel(logging.INFO)
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try:
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url = f"https://public-asai-dl-models.s3.eu-central-1.amazonaws.com/DeepPhonemizer/{file}"
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directory = os.path.join(torch.hub.get_dir(), "checkpoints")
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os.makedirs(directory, exist_ok=True)
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path = os.path.join(directory, file)
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if not os.path.exists(path):
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dl_kwargs = {} if dl_kwargs is None else dl_kwargs
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download_url_to_file(url, path, **dl_kwargs)
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return Phonemizer.from_checkpoint(path)
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finally:
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logger.setLevel(orig_level)
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def _unnormalize_waveform(waveform: torch.Tensor, bits: int) -> torch.Tensor:
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r"""Transform waveform [-1, 1] to label [0, 2 ** bits - 1]"""
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waveform = torch.clamp(waveform, -1, 1)
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waveform = (waveform + 1.0) * (2**bits - 1) / 2
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return torch.clamp(waveform, 0, 2**bits - 1).int()
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def _get_taco_params(n_symbols):
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return {
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"mask_padding": False,
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"n_mels": 80,
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"n_frames_per_step": 1,
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"symbol_embedding_dim": 512,
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"encoder_embedding_dim": 512,
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"encoder_n_convolution": 3,
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"encoder_kernel_size": 5,
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"decoder_rnn_dim": 1024,
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"decoder_max_step": 2000,
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"decoder_dropout": 0.1,
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"decoder_early_stopping": True,
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"attention_rnn_dim": 1024,
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"attention_hidden_dim": 128,
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"attention_location_n_filter": 32,
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"attention_location_kernel_size": 31,
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"attention_dropout": 0.1,
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"prenet_dim": 256,
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"postnet_n_convolution": 5,
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"postnet_kernel_size": 5,
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"postnet_embedding_dim": 512,
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"gate_threshold": 0.5,
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"n_symbol": n_symbols,
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}
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def _get_wrnn_params():
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return {
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"upsample_scales": [5, 5, 11],
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"n_classes": 2**8, # n_bits = 8
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"hop_length": 275,
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"n_res_block": 10,
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"n_rnn": 512,
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"n_fc": 512,
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"kernel_size": 5,
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"n_freq": 80,
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"n_hidden": 128,
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"n_output": 128,
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}
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